Abstract:

A method for modeling an investment significant parameter of a financial
instrument, using a computer. At least one series of historical bid
prices of the financial instrument or historical ask prices of the
financial instrument is provided. A financial model type that has at
least one variable parameter is selected. The variable parameter(s) of
the selected financial model type is initialized. The series of
historical bid prices and/or historical ask prices is applied to the
initialized financial model type to estimate the variable parameter(s).
The resulting model of the financial instrument may be used to predict
future values of the investment significant parameter of the financial
instrument. These predicted future values may be used to determine
whether to perform automated trades of the financial instrument.

Claims:

1. A method for performing automated trades of at least one financial
instrument, using a computer, the method comprising the steps of:a)
predicting at least one future value of a numerical investment
significant parameter of each of the at least one financial instrument,
the at least one future value of the numerical investment significant
parameter of each financial instrument predicted using at least one of a
bid stream of the bid prices of a corresponding financial instrument or
an ask stream of the ask prices of the corresponding financial
instrument;b) for each of the at least one financial instrument,
comparing the at least one future value of the numerical investment
significant parameter predicted in step (a) to at least one most recent
value of the numerical investment significant parameter of the
corresponding financial instrument to determine trend data of the
corresponding financial instrument;c) automatically placing a buy order
for one of the financial instruments modeled in step (a) if the
corresponding trend data determined in step (b) meets a buy criterion for
the one of the financial instruments; andd) automatically placing a sell
order for one of the financial instruments modeled in step (a) if the
corresponding trend data determined in step (b) meets a sell criterion
for the one of the financial instruments.

2. The method according to claim 1, wherein for each financial instrument,
the at least one of the bid stream or the ask stream used in step (a) is
at least one of a real time bid stream of the bid prices of the
corresponding financial instrument or a real time ask stream of the ask
prices of the corresponding financial instrument.

3. The method according to claim 1, wherein, for each financial
instrument, step (a) includes the steps of:a1) providing a corresponding
model of at least one numerical investment significant parameter of the
financial instrument based on a set of historical quotes of at least one
of bid prices or ask prices of each financial instrument;a2) selecting at
least one of a corresponding real time bid stream or a corresponding real
time ask stream based on the at least one of bid prices or ask prices of
the financial instrument modeled by the corresponding model;a3) applying
the selected at least one of the corresponding bid stream or the
corresponding ask stream to the corresponding model; anda4) operating the
corresponding model to predict the at least one future value of the
numerical investment significant parameter of the financial instrument.

4. The method according to claim 3, wherein, for each model corresponding
to one of the financial instruments, step (a1) includes the steps of:a1a)
providing the corresponding set of historical quotes of at least one of
bid prices or ask prices;a1b) selecting a corresponding financial model
type having at least one variable parameter;a1c) initializing the at
least one variable parameter of the selected financial model type;
anda1d) generating the model of the at least one future value of the
numerical investment significant parameter of the corresponding financial
instrument by applying the corresponding set of historical quotes to the
initialized financial model type and estimating the at least one variable
parameter.

5. The method according to claim 4, wherein, for each model corresponding
to one of the financial instruments:step (a1a) includes providing at
least one of:a corresponding historical time series of the bid prices of
the financial instrument, including corresponding bid times; ora
corresponding historical time series of the ask prices of the financial
instrument, including corresponding ask times;step (a1d) includes
applying the at least one of the corresponding historical time series of
bid prices or the corresponding historical time series of ask prices to
the initialized financial model type to estimate the at least one
variable parameter;step (a3) includes the steps of;a3a) providing the
selected at least one of;the corresponding bid stream of the financial
instrument, including the bid prices and corresponding bid times; orthe
corresponding ask stream of the financial instrument, including the ask
prices and corresponding ask times; anda3b) applying the selected at
least one of the corresponding bid stream or the corresponding ask stream
to the model; andstep (a4) includes operating the model to predict one or
more corresponding future values of the numerical investment significant
parameter of the financial instrument including a predicted time of each
of the one or more future values.

6. The method according to claim 5, wherein, for each model corresponding
to one of the financial instruments, the predicted time of each
corresponding future value of the numerical investment significant
parameter is within a predetermined period of time after its prediction.

7. The method according to claim 4, wherein, for each model corresponding
to one of the financial instruments:step (a1a) includes the steps
of:a1a1) providing a corresponding historical time series of the bid
prices of the financial instrument, including corresponding bid times,
and a corresponding historical time series of the ask prices of the
financial instrument, including corresponding ask times; anda1a2)
calculating a corresponding historical time series of a spread between
the bid prices and the ask prices of the financial instrument from the
corresponding historical time series of the bid prices and the
corresponding historical time series of the ask prices; andstep (a1d)
includes applying the corresponding historical time series of the bid
prices, the corresponding historical time series of the ask prices, and
the corresponding historical time series of the spread between the bid
prices and the ask prices to the initialized financial model type to
estimate the at least one variable parameter.

8. The method according to claim 7, wherein, for each model corresponding
to one of the financial instruments, step (a3) includes:a3a) providing
the corresponding bid stream of the bid prices of the financial
instrument, including corresponding bid times, and the corresponding ask
stream of the ask prices of the financial instrument, including
corresponding ask times;a3b) calculating a corresponding spread stream of
a spread between the bid prices and the ask prices of the financial
instrument from the corresponding bid stream and the corresponding ask
stream; anda3c) applying the corresponding bid stream, the corresponding
ask stream, and the corresponding spread stream to the model.

9. The method according to claim 4, wherein, for each model corresponding
to one of the financial instruments, step (a1b) includes selecting the
corresponding financial model type to be one of: an expert system model;
a linear analytic model; a non-linear analytic model; a chaotic model; a
neural network model; a time delay neural network model; a Markov-chain
Monte Carlo model; a wavelet transformation model; a regression model; a
fractal model; a support vector machine model; or a Bayesian model.

10. The method according to claim 3, wherein, for each of the financial
instruments, step (a3) includes the steps of:a3a) providing the
corresponding bid stream of the bid prices of the financial instrument,
including corresponding bid times, and the corresponding ask stream of
the ask prices of the financial instrument, including corresponding ask
times;a3b) calculating a corresponding spread stream of a spread between
the bid prices and the ask prices of the financial instrument from the
corresponding bid stream and the corresponding ask stream;a3c)
identifying outlier bids in the corresponding bid stream of the bid
prices using the corresponding spread stream;a3d) removing the identified
outliers bids and corresponding bid times from the corresponding bid
stream before applying the corresponding bid stream to the corresponding
model;a3e) identifying outlier asks in the corresponding ask stream of
the ask prices using the corresponding spread stream; anda3f) removing
the identified outliers asks and corresponding ask times from the
corresponding ask stream before applying the corresponding ask stream to
the corresponding model.

11. The method according to claim 1, wherein, for each financial
instrument, step (a) includes the steps of:a1) providing a corresponding
model of at least one of bid prices or ask prices of the financial
instrument based on a corresponding set of historical quotes of the at
least one of bid prices or ask prices of each financial instrument;a2)
selecting at least one of a corresponding real time bid stream or a
corresponding real time ask stream based on the at least one of bid
prices or ask prices of the financial instrument modeled by the
corresponding model;a3) applying the selected at least one of the
corresponding bid stream or the corresponding ask stream to the
corresponding model; anda4) operating the corresponding model to predict
at least one of corresponding future bid prices of the financial
instrument or corresponding future ask prices of the financial
instrument.

12. The method according to claim 11, wherein, for each model
corresponding to one of the financial instruments, step (a1) includes the
steps of:a1a) providing the corresponding set of historical quotes of at
least one of bid prices or ask prices;a1b) selecting a corresponding
financial model type having at least one variable parameter;a1c)
initializing the at least one variable parameter of the selected
financial model type; anda1d) generating the model of the at least one of
bid prices or ask prices of the corresponding financial instrument by
applying the corresponding set of historical quotes to the initialized
financial model type and estimating the at least one variable parameter.

13. The method according to claim 12, wherein, for each model
corresponding to one of the financial instruments:step (a1a) includes
providing at least one of:a corresponding historical time series of the
bid prices of the financial instrument, including corresponding bid
times; ora corresponding historical time series of the ask prices of the
financial instrument, including corresponding ask times;step (a1d)
includes applying the at least one of the corresponding historical time
series of bid prices or the corresponding historical time series of ask
prices to the initialized financial model type to estimate the at least
one variable parameter;step (a3) includes the steps of;a3a) providing the
selected at least one of;the corresponding bid stream of the financial
instrument, including the bid prices and corresponding bid times; orthe
corresponding ask stream of the financial instrument, including the ask
prices and corresponding ask times; anda3b) applying the selected at
least one of the corresponding bid stream or the corresponding ask stream
to the model; andstep (a4) includes operating the model to predict at
least one of;one or more corresponding future bid prices of the financial
instrument including a predicted bid time of each of the one or more
future bid prices; orone or more corresponding future ask prices of the
financial instrument including a predicted ask time of each of the one or
more future ask prices.

14. The method according to claim 13, wherein, for each model
corresponding to one of the financial instruments, the predicted bid time
of each corresponding future bid price and the predicted ask time of each
corresponding future ask price are within a predetermined period of time
after their prediction.

15. The method according to claim 1, wherein the at least one financial
instrument is at least one publicly traded financial instrument.

16. The method according to claim 1, wherein the at least one financial
instrument are selected from the group consisting of stocks, bonds,
commodities, currencies, equities, derivatives, and futures.

17. The method according to claim 1, wherein, for each of the financial
instruments, step (a) includes predicting one or more corresponding
future values of the numerical investment significant parameter of the
financial instrument including a corresponding parameter confidence level
of each of the one or more corresponding future values.

18. The method according to claim 17, wherein the trend data determined in
step (b) for each of the financial instruments includes a corresponding
trend confidence level based on at least one of the corresponding
parameter confidence level.

19. The method according to claim 18, wherein the buy criterion and the
sell criterion for each of the financial instruments varies based on the
corresponding trend confidence level determined in step (b).

20. The method according to claim 18, wherein:step (c) includes the steps
of:c1) determining if the corresponding trend data determined in step (b)
meets the buy criterion for the one of the financial instruments;c2)
determining a buy size of the buy order based on the corresponding trend
confidence level determined in step (b) when the corresponding trend data
is determined to meet the buy criterion in step (c1); andc3)
automatically placing the buy order for the one of the financial
instruments when the corresponding trend data is determined to meet the
buy criterion in step (c1); andstep (d) includes the steps of:d1)
determining if the corresponding trend data determined in step (b) meets
the sell criterion for the one of the financial instruments;d2)
determining a sell size of the sell order based on the corresponding
trend confidence level determined in step (b) when the corresponding
trend data is determined to meet the sell criterion in step (d1); andd3)
automatically placing the sell order for the one of the financial
instruments when the corresponding trend data is determined to meet the
sell criterion in step (d1).

21. The method according to claim 1, wherein:the at least one investment
significant parameter of the financial instrument includes at least one
of a future bid price or a future ask price; andfor each of the financial
instruments, step (a) includes predicting at least one of:one or more
corresponding future bid prices of the financial instrument including a
corresponding bid confidence level of each of the one or more
corresponding future bid prices; orone or more corresponding future ask
prices of the financial instrument including a corresponding ask
confidence level of each of the one or more corresponding future ask
prices.

22. The method according to claim 1, wherein:the at least one financial
instrument is a plurality of financial instruments; andstep (b) includes
the steps of:b1) for each of the at least one financial instrument,
comparing the at least one investment significant parameter predicted in
step (a) to at least one most recent most recent value of the numerical
investment significant parameter of the corresponding financial
instrument to determine a predicted change of the corresponding financial
instrument;b2) analyzing the plurality of predicted changes determined in
step (b1) to formulate a joint trade strategy for the plurality of
financial instruments over a predetermined period time; andb3) setting
the trend data corresponding to each of the financial instruments based
on the joint trade strategy.

23. The method according to claim 22, wherein step (b2) includes analyzing
the plurality of predicted changes determined in step (b1) to formulate
the joint trade strategy using one of: an expert system model; a linear
analytic model; a non-linear analytic model; a chaotic model; a neural
network model; a time delay neural network model; a Markov-chain Monte
Carlo model; a wavelet transformation model; a regression model; a
fractal model; a support vector machine model; or a Bayesian model.

24. The method according to claim 1, wherein:the at least one investment
significant parameter of the financial instrument includes at least one
of a future bid price or a future ask price;the at least one financial
instrument is a plurality of financial instruments; andstep (b) includes
the steps of:b1) for each of the at least one financial instrument,
comparing the at least one future bid price or future ask price predicted
in step (a) to at least one most recent bid price or at least one most
recent ask price of the corresponding financial instrument to determine a
predicted quote change of the corresponding financial instrument;b2)
analyzing the plurality of predicted quote changes determined in step
(b1) to formulate a joint trade strategy for the plurality of financial
instruments over a predetermined period time; andb3) setting the quote
trend data corresponding to each of the financial instruments based on
the joint trade strategy.

25. The method according to claim 1, wherein:the at least one financial
instrument is a plurality of financial instruments;step (c) includes the
steps of:c1) determining the buy criterion for each of the financial
instruments based on at least one trend data determined in step (b)
corresponding to another of the financial instruments;c2) for each
financial instrument, determining if the corresponding trend data
determined in step (b) meets the corresponding buy criterion determined
in step (c1); andc3) automatically placing the buy order for each
financial instrument for which the corresponding trend data is determined
to meet the corresponding buy criterion in step (c2); andstep (d)
includes the steps of:d1) determining the sell criterion for each of the
financial instruments based on at least one trend data determined in step
(b) corresponding to another of the financial instruments;d2) for each
financial instrument, determining if the corresponding trend data
determined in step (b) meets the corresponding sell criterion determined
in step (d1); andd3) automatically placing the sell order for each of the
financial instruments for which the corresponding trend data is
determined to meet the corresponding buy criterion in step (d2).

26. The method according to claim 1, wherein:step (c) includes the steps
of:c1) comparing the corresponding trend data determined in step (b) to
the buy criterion for the one of the financial instruments;c2)
determining a buy size of the buy order based on the comparison of the
corresponding trend data and the buy criterion in step (c1);c3)
automatically placing the buy order for the one of the financial
instruments when the buy size determined in step (c2) is greater than
zero; andstep (d) includes the steps of:d1) comparing the corresponding
trend data determined in step (b) to the sell criterion for the one of
the financial instruments;d2) determining a sell size of the sell order
based on the comparison of the corresponding trend data and the sell
criterion in step (d1);d3) automatically placing the sell order for the
one of the financial instruments when the sell size determined in step
(d2) is greater than zero.

27. The method according to claim 1, wherein the investment significant
parameter includes at least one of: a future trade price; a future bid
price; a future ask price; a future spread; a fair market value (FMV); an
expected profit; a change in trade price between two times; a change in
bid price between two times; a change in ask price between two times; a
change in spread between two times; a change in FMV between two times; a
change in profit between two times; a rate of change of trade price; a
rate of change of bid price; a rate of change of ask price; a rate of
change of spread; a rate of change of FMV; a rate of change of profit; a
prediction of winners and losers; or a buy/sell instruction.

[0002]The present invention concerns a method and system of modeling
financial instruments using bid and ask prices. In particular, this
method and system may allow for improved prediction of future bid and ask
prices of financial instruments and may be used to provide information to
make decisions for automated trading of various financial instruments.

BACKGROUND OF THE INVENTION

[0003]A fundamental analysis strategy is the investment in stocks on the
basis of the value of the companies represented by the stocks. The
company's balance sheet, income statement, etc., are studied to help
determine the financial and market position of the company. If the
analysis of the company's historic growth and profit patterns shows a
steadily growing organization, and the research of the organization and
its markets show a company that is competent and sound, a fundamental
analysis approach may conclude that the company should continue to grow
and prosper.

[0004]On the other hand, a technical analysis strategy involves trying to
make profits based on the short-term swings of the market, such as, for
example, day traders, who try to take advantage of hourly or daily price
changes to make a profit. Slightly longer-term technical analysis
investors track stock price and trading volume fluctuations over a period
of a few days or weeks and trade on the basis of recent trends. As
opposed to fundamental analysis where the emphasis is on the strength of
the underlying corporation, technical analysis focuses on patterns that
appear on the historical price charts of a specific stock and of the
stock market in general in order to help predict the future of that
stock's price. This strategy is based on the theory that certain patterns
of stock prices tend to repeat themselves over time.

[0005]The Internet provides a great variety of uses including the buying
and selling of financial instruments. The Internet has become a major
means by which investors and brokers can both monitor the stock market
and buy and sell stocks.

[0006]Although an investor does not need to be online to buy stocks,
Internet access may be of great value. The Internet offers resources that
are unmatched by any single print source. A wired investor can get access
to literally thousands of investment services, publications, newsletters,
and discussion groups. In this manner an investor can quickly gather a
large amount of information about various financial instruments,
including information about companies whose stock may be of interest.

[0007]The stock market includes a number of features that affect the stock
investor. One of these features is the existence of agents to facilitate
the functioning of the market. Market makers, specialists and Electronic
Communications Networks (ECNs) make market in stocks. Market makers are
part of the National Association of Securities Dealers market (NASD), and
specialists work on the New York Stock Exchange (NYSE) and other listed
exchanges. An ECN is an electronic board where buy and sell orders may be
posted by any investor worldwide. These agents serve a similar function
but there are a number of differences between them.

[0008]The New York Stock Exchange (NYSE) is the oldest stock exchange in
the United States. The NYSE (as well as the Philadelphia, Chicago,
Boston, and Pacific Stock Exchanges) uses an agency auction market system
that is designed to allow the public to meet the public as much as
possible. The majority of trading volume (approximately 90%) occurs with
no intervention from the specialist. The responsibility of specialists is
to make a fair and orderly market in the issues assigned to them. They
must yield to public orders, which means that they may not trade for
their own account when there are public bids and asks better than their
own. The specialist has an affirmative obligation to eliminate imbalances
of supply and demand when they occur. Specialists are required to make a
continuous market. The exchange has strict guidelines for trading depth
and continuity that must be observed. Specialists are subject to fines
and censures if they fail to perform this function. NYSE specialists have
large capital requirements and are overseen by Market Surveillance at the
NYSE.

[0009]A specialist will typically maintain a narrow spread between offers
to buy and offers to sell. Generally, the trader will need access to a
professional's data feed before the trader can really see the size of the
spread.

[0010]There are over a thousand NYSE members (i.e., seats), of which
approximately a third are specialists. There are over 3000 common and
preferred stocks listed on the NYSE. On the average, each specialist
handles 6 issues. The very big stocks may have a specialist devoted
solely to them.

[0011]Every listed stock has one firm assigned to it on the floor. Most
stocks are also listed on regional exchanges in San Francisco, Chicago,
Philadelphia and Boston. All NYSE trading (approximately 80% of total
volume) occurs at that post on the floor of the specialist assigned to
it.

[0012]The National Association of Securities Dealers Automated Quotation
system (NASDAQ) is an interdealer market represented by over 600
securities dealers trading more than 15,000 different issues. These
dealers are called market makers. Unlike the NYSE, the NASDAQ market does
not operate as an auction market. Instead, market makers are expected to
compete against each other by posting the best quotes (best bid, i.e.,
best offer to buy, and best ask, i.e., best offer to sell).

[0013]A NASDAQ Level II quotation system shows all the bid offers, ask
offers, size of each offer (the order size), and the market makers making
the offers. The order size is simply the number of shares the market
maker is prepared to fill at that price. Since about 1985 the average
person has had access to Level II quotes.

[0014]The Small Order Execution System (SOES) was implemented by NASDAQ
following the 1987 market crash. This system is intended to help the
small investor have his or her transactions executed without allowing
market makers to take advantage of the small investor. The trader may see
mention of "SOES Bandits" which is slang for people who day-trade stocks
on the NASDAQ using the SOES, scalping profits on the spreads.

[0015]A firm can become a market maker on NASDAQ by applying to NASD. The
requirements include certain capital requirements, electronic interfaces,
and a willingness to make a two-sided market. The trader must be there
every day. If the trader doesn't post continuous bids and asks every day
the trader can be penalized and not allowed to make a market for a month.
Market makers are regulated by the NASD, which is overseen by the SEC.

[0016]The brokerage firm can handle customer orders either as a broker or
as a dealer/principal. When the firm acts as a broker, it simply arranges
the trade between buyer and seller, and charges a commission for its
services. When the firm acts as a dealer/principal, it's either buying
for or selling from its own account (to or from the customer), or acting
as a market maker. The customer is charged either a mark-up or a
mark-down, depending on whether they are buying or selling. The firm is
disallowed from charging both a mark-up (or mark-down) and a commission.
Whether acting as a broker or as a dealer/principal, the brokerage is
required to disclose its role in the transaction. However,
dealers/principals are not necessarily required to disclose the amount of
the mark-up or mark-down, although most do this automatically on the
confirmation as a matter of policy. Despite its role in the transaction,
the firm must be able to display that it made every effort to obtain the
best posted price. Whenever there is a question about the execution price
of a trade, it is usually best to ask the firm to produce a Time and
Sales report, which allows the customer to compare all execution prices
with the actual execution price reported to the customer.

[0017]In NASDAQ, the public almost always trades with the dealer as a
counterparty instead of another public investor, making it nearly
impossible to buy on the bid or sell on the ask. Dealers can buy on the
bid even though the public is bidding at the same price. Despite the
requirement of making a market, in the case of market makers as opposed
to specialists, there is no one firm who has to take responsibility if
trading is not fair or orderly, as what seemed to have happened during
the crash of 1987. At that time, many NASD firms simply stopped making
markets or answering phones until prices were less volatile.

[0018]Recently, Electronic Communication Networks (ECN) were established
in order to allow investors to trade NASDAQ listed stocks without having
to go through market makers, oftentimes resulting in better prices for
the investor. An ECN is an electronic system where buy and sell orders
may be posted by any investor worldwide, where any investor or dealer may
trade against that order. The best bid and best ask orders from the ECN
are posted in the NASDAQ system alongside those of market makers.

[0019]If a trader wants to buy or sell a financial instrument, such as a
stock or other security, in an open market, the trader normally trades
via firms who act as agents who specialize in that particular security.
These firms stand ready to sell the trader a security at the asking price
(the "ask"). Or, if the trader owns the security and would like to sell
it, the agent buys the security from the trader at the bid price (the
"bid"). The bid and the ask prices remain until a new price is set. The
difference between the current bid and the current ask is called the
spread. Financial instruments that are heavily traded tend to have very
narrow spreads (e.g., a few cents), but financial instruments that are
lightly traded may have spreads that are significant, even as high as
several dollars.

[0020]The width of the spread is indicative of the financial instrument's
liquidity. Liquidity basically measures the aggregate quantity investors
are willing to buy or sell of the financial instrument at any time. In
the stock market, market makers or specialists (depending on the
exchange) buy stocks from the public at the bid and sell stocks to the
public at the ask (called "making a market in the stock"). At most times
(unless the market is crashing, etc.) these people stand ready to make a
market in most stocks and often in substantial quantities, thereby
maintaining market liquidity. Dealers earn profit by realizing a large
part of the spread on each transaction--they normally are not long-term
investors.

[0021]Two types of online trading available to the public are: Internet
trading provided by firms that route a customer's order to a trading desk
or to a third party willing to pay for order flow; and dedicated online
services provided by firms where customer's orders go directly to the
exchange or ECN offering direct execution.

[0022]If the online investor uses the first type of online trading
discussed above, the customer's order may be gamed by a specialist or
market maker handling the order. Unfortunately, if this happens to the
customer, they may not be able to recognize that it has happened from the
minimal information typically provided in the order confirmation.
Typically, this type of customer only has access to what's called Level I
data--the best bid, the best ask, the last trade, and the order size of
each data respectively.

[0023]If the customer uses the second type of online trading discussed
above (i.e., the order goes from the firm directly to the exchange), the
customer most likely is looking at a NASDAQ Level II screen. This screen
shows all the bid offers, ask offers, the recent trades, the size of each
offer or trade, and the market makers and ECNs making the offers.

[0024]An online trader connected to a web site that has a screen that
displays NASDAQ Level II data, may see the following information
streaming continuously on the screen: all bid offers, all ask offers, all
trades, the size of each offer or trade and the market maker or ECN
making the offer. This data may be refreshed as often as ten times per
second. Hence, many traders are continuously analyzing the data on their
screen all day. Moreover, unless the trader has a prodigious memory and
even then the information may arrive too quickly to be fully read, much
less utilized by the trader. The more individual financial instruments
monitored by the trader, the greater the difficulty in utilizing the
flood incoming data. Thus, a lot of important information may escape
notice. Additionally, impatience at waiting for the desired trading
condition may cause the trader to make a trade at an inopportune moment.
Thus, an automated means of analyzing this wealth of information is
desirable.

[0025]The present invention involves methods of modeling financial markets
and automating trades to take advantage of this plethora of bid and ask
price data.

SUMMARY OF THE INVENTION

[0026]An exemplary embodiment of the present invention is a method and
system for modeling an investment significant parameter of a financial
instrument, using a computer. At least one series of historical bid
prices of the financial instrument or historical ask prices of the
financial instrument is provided. A financial model type that has at
least one variable parameter is selected. The variable parameter(s) of
the selected financial model type is initialized. The series of
historical bid prices and/or historical ask prices is applied to the
initialized financial model type to estimate the variable parameter(s).

[0027]Another exemplary embodiment of the present invention is a method
and system for predicting future bid prices and/or future ask prices of a
financial instrument, using a computer. A model of at least one of the
bid prices or the ask prices of the financial instrument based on a set
of historical quotes of the bid prices and/or ask prices is provided. At
least one of a bid stream of the bid prices of the financial instrument
or an ask stream of the ask prices of the financial instrument is
selected based on which of the bid prices and/or ask prices of the
financial instrument are modeled by the model. The selected bid stream
and/or ask stream is applied to the model. The model is operated to
predict at least one future bid price and/or future ask price of the
financial instrument based on the applied bid and/or ask stream.

[0028]A further exemplary embodiment of the present invention is a method
and system for performing automated trades of at least one financial
instrument, using a computer. At least one future bid price or future ask
price of each of the financial instruments is predicted. The future bid
price and/or future ask price of each financial instrument is predicted
using at least one of a bid stream of the bid prices of the corresponding
financial instrument or an ask stream of the ask prices of the
corresponding financial instrument. For each of the at least one
financial instrument, the predicted future bid and/or ask price(s) are
compared to at least one most recent bid and/or ask price of the
corresponding financial instrument to determine quote trend data of the
corresponding financial instrument. If the quote trend data of one of the
financial instruments meets a buy criterion for that financial
instrument, a buy order is automatically placed for the financial
instrument; and if the quote trend data of one of the financial
instruments meets a sell criterion for that financial instrument, a sell
order is automatically placed for the financial instrument.

BRIEF DESCRIPTION OF THE DRAWINGS

[0029]The invention is best understood from the following detailed
description when read in connection with the accompanying drawings.
Included in the drawings are the following figures:

[0030]FIG. 1 is a flow chart illustrating an exemplary method and system
of modeling an investment significant parameter of a financial instrument
according to the present invention.

[0031]FIG. 2 is a flow chart illustrating an exemplary method and system
of predicting future bid prices and/or future ask prices of a financial
instrument according to the present invention.

[0032]FIG. 3 is a flow chart illustrating an exemplary method and system
of performing automated trades of at least one financial instrument
according to the present invention.

DETAILED DESCRIPTION OF THE INVENTION

[0033]Exemplary embodiments of the present invention includes methods and
system of modeling financial instruments to predict future prices of
these financial instruments as part of exemplary technical analysis
investment strategies. These exemplary technical analysis investment
strategies may also include the automated placing of buy and sell orders
for a portfolio including one or more different financial instruments.
These financial instruments may include: stocks, bonds, commodities,
currencies, equities, derivatives, and/or futures.

[0034]In technical analysis investment strategies, one important
consideration is the type of financial model to use. A large number of
different types of financial models have been created, such as: expert
system models; linear analytic models; non-linear analytic models;
chaotic models; neural network models; time delay neural network models;
Markov-chain Monte Carlo models; wavelet transformation models;
regression models; fractal models; support vector machine models; and
Bayesian models. Specific applications of these types of financial models
based on trade prices are known to those skilled in the art. For example,
Carol Alexander describes a number of specific financial models in
chapters 3-13 of MARKET MODELS: A GUIDE TO FINANCIAL DATA ANALYSIS (John
Wiley & Sons, Ltd. (2001)).

[0035]Each model type includes at least one variable parameter that may be
used to match the model to the behavior of the financial instrument.
Potential variable parameters may include a numerical value, a string to
be optimized, a logical value, a conditional rule, and/or a structural
link. For example, in wavelet transformation models, the wavelet
transform coefficients are variable parameters, while in expert system
models and Bayesian models the variable parameters may include likelihood
values and/or decision rules and in neural network models the links
between nodes may used as variable parameters.

[0036]Additionally, each model type may present a number of advantages and
a number of disadvantages when used to model the behavior of a financial
instrument. For example, neural network models may take less time to
build as compared to many other models, but because these models are
essentially black boxes and not causal. Thus, if a neural network model
diverges, it may be difficult to identify this divergence quickly as the
exact process used to achieve the output is hidden. Still, the ability of
neural network models to handle non-linear data make such models
attractive for modeling financial instruments.

[0037]As another example, chaotic and fractal models may also be
attractive for their ability to handle non-linear data, but errors in
initial values of these models may lead to exponential divergence over
time. Thus, chaotic and fractal models of the financial instrument may
require monitoring and occasional re-initialization to maintain their
accuracy. This re-initialization procedure typically does not pose a
problem in modeling many financial instruments where a stream of new
input data may be available to update the initialization almost
continuously.

[0038]Other considerations for the technical analysis investor include the
type of data to apply to the model and the type of data to be determined
by the model. Many financial models have been created that utilize
historic trade price data as input data.

[0039]Trade price data has seemed an obvious choice for the input data of
a financial model. This data includes the actual prices paid for the
financial instrument previously. Additionally, the amount of data is
relatively tractable. As described above, NASDAQ Level II data for a
stock may be refreshed as often as ten times a second, but the majority
of these updates are due to changes in the bid and ask prices of the
stock. Actual trades of the stock occur much less frequently. Thus, new
trade prices are posted at a significantly lower rate.

[0040]Unfortunately, some trades take place at prices that are much higher
or lower than might be expected. These unusual trade prices may be due to
trade-specific concerns, such as gaining a controlling share in a
company, tax issues, etc., that are not included in the model. If not
removed from the input data, these unusual trade prices may significantly
affect the accuracy of the financial model, but such outlying trades may
be difficult to identify, particularly in a low trade volume financial
instrument. Additionally, depending on the financial market, the trade
prices may not be posted for period time after the trade occurs. Even in
the tightly regulated stock market, there is typically a one to ten
second delay before a trade price is posted. Depending on the volatility
of the financial instrument, the effect of this delay of the technical
analysis investor may range from a minimal issue to an extreme detriment.

[0041]In the exemplary embodiments of the present invention, the actual
series of bid and/or ask prices of the financial instrument are among the
data applied to the model. The bid and ask prices (quotes) provide a
number of advantages as input data for a financial model. Unlike trade
prices, which represent unique events, bid and ask prices represent a
continuous, ongoing record of the market. At any given time, each
financial instrument has one, and only one, current best bid price and
one, and only one, current best ask price. These quotes remain valid
until replaced by a new quote, at which time the old quote is no longer
valid. Thus, because new quotes do not become effective until posted,
access to new bid and ask prices is nearly instantaneous. This may allow
a financial model based on streams of quotes to operate substantially in
real time.

[0042]In a low trade volume financial instrument, the time between trades
may be days or even longer, potentially leading to large fluctuations
between consecutive trade prices and correspondingly large uncertainty
for a technical analysis investor. The quotes may move numerous times
between trades as potential buyers and sellers seek an appropriate deal,
which allows exemplary embodiments of the present invention to more
accurately predict investment significant parameters of financial
instruments. Investment significant parameters may include any
predictions that are desirable to formulate an investment strategy, for
example: future trade prices; future bid prices; future ask prices;
future spreads; fair market values (FMV) now and/or in the future;
expected profit; changes in prices, spreads, FMV's, or profit between two
times; rates of change in prices, spreads, FMV's, or profit; winners and
losers; and buy/sell instructions. The same advantages may exist for high
trade volume financial instruments as well, only on a different time
scale.

[0043]Additionally, the bid prices and the ask prices of a given financial
instrument tend to track one another, thereby maintaining a relatively
constant spread for the financial instrument, as described above for
stocks. Therefore using both the bid and ask prices in a model may
simplify the identification of outlying bid and ask prices. For example,
a sudden jump in the ask price which is not followed by a corresponding
increase in the bid price to take advantage of the increased price the
market is willing to pay is likely to indicate a misquote or other error.
However, if the increased ask price is real, the bid price is likely to
quickly follow. Thus, exemplary embodiments of the present invention also
may allow technical analysis investors to track sudden changes in a
financial instrument with greater certainty.

[0044]FIG. 1 illustrates an exemplary method for modeling an investment
significant parameter of a financial instrument, using a computer,
according to the present invention. The financial instrument may
desirably be a publicly traded financial instrument, but this is not
necessary. It may be any type of financial instrument including: a stock;
a bond; a commodity; a currency; an equity; a derivative security, or a
future.

[0045]At least one series of historical bid prices of the financial
instrument or historical ask prices of the financial instrument is
provided as training data to the model, step 100. The provided series of
historical bid and/or ask prices of the financial instrument may
desirably include corresponding bid or ask sizes, respectively,
associated with the quotes. Additionally, the provided series of
historical quotes may desirably be provided as time series of the
historical bid and/or ask prices including corresponding bid or ask
times, respectively. Further a series of historical spreads between the
historical bid prices and the historical ask prices of the financial
instrument may be provided as well.

[0046]It may also be desirable for the historical series of quotes to
include at least one complete consecutive series of: historical bid
and/or ask prices of the financial instrument spanning a predetermined
period of time; or a predetermined number of historical bid and/or ask
prices of the starting from a predetermined time.

[0047]Outlying quotes may desirably be removed from the provided series of
historical quotes, including complete consecutive series, before the
series is (are) applied to the model.

[0048]An exemplary method of removing outliers from the provided series of
historical quotes uses the relative stability of the spread between bid
prices and ask price of financial instruments discussed above. Time
series of the historical bid prices of the financial instrument,
including corresponding bid times, and the historical ask prices of the
financial instrument, including corresponding ask times, are provided. A
time series of the spread between the historical bid and ask prices is
calculated from their historical time series. Outlier bids in the time
series of the historical bid prices may be identified using the time
series of the spread, as may outlier asks in the time series of the
historical ask prices. Quotes that cause the spread to increase, or
decrease, beyond certain thresholds that may vary from financial
instrument to financial instrument, may indicate potential outliers. If
the change in the spread is quickly corrected by a change in the same
quote (e.g. an unusually large jump in the bid price is followed by a
corresponding drop in the bid price), the quote in question may be
identified as an outlier. Conversely, if the change in the spread is
corrected by a change in the other quote (e.g. an unusually large jump in
the bid price is followed by a corresponding jump in the ask price), the
quote in question may indicate the beginning of a trend up or down in the
value of the financial instrument. Once identified the outliers bid(s)
and/or ask(s) and their corresponding bid or ask times are removed from
the time series.

[0049]A financial model type is selected, step 102. As discussed above,
numerous financial model types exist, each with its own advantages and
disadvantages, depending on the financial instrument to be modeled. One
skilled in the art may understand that each financial model type has at
least one variable parameter that may be tuned to model the behavior of a
particular financial instrument. The variable parameter(s) of the
selected financial model type are initialized, step 104. This
initialization may be based on a priori knowledge of the financial
instrument and/or the selected financial model type, initial values of
the series of historical quotes provided in step 100, a predetermined
initial setting, or a combination of these methods.

[0050]The series of historical bid prices and/or historical ask prices are
applied to the initialized financial model type as training data to
estimate the variable parameter(s), step 106. In an exemplary embodiment
of the present invention, the financial model type selected in step 102
is used to calculate at least one of a predicted bid price or a predicted
ask price of the financial instrument. This calculation may be based on a
set of historical quotes that includes a predetermined quantity of
consecutive historical quotes from the series of historical bid prices
and/or historical ask prices provided in step 100. The calculation may be
repeated for each set of historical quotes to calculate a plurality of
predicted bid prices and/or a plurality of predicted ask prices. These
predicted quotes may then be compared to the series of historical quotes
provided in step 100. The variable parameter of the selected financial
model type is varied based on the differences between the predicted
quotes and the series of historical quotes. The calculations and
comparisons may be repeated until the variable parameter has been
estimated and the behavior of the financial model matches the historical
behavior of the financial instrument to within a predetermined degree of
accuracy, i.e. the predicted quotes substantially correspond to the
series of historical quote.

[0051]It is noted that the use of the training data provided in step 100
may depend on the type of financial model selected in step 102. For
example, in a non-linear analytic model differences between the output of
the model and the series of historical quotes may be used as feedback in
an estimation maximization algorithm or other recursive algorithm to
adjust the model parameters. In another example, time series of
historical quote prices and corresponding quote times may be used as
training data for a time delay neural network model of the financial
instrument.

[0052]It is noted that other training data which may be provided in step
100, such as: a time series of the spread between the historical bid
prices and the historical ask prices; a series of historical trade
prices; and/or extrinsic data, including market indices or related
financial instruments, may also be applied to the selected financial
model type to improve the estimation of the variable parameter(s).

[0053]FIG. 2 illustrates an exemplary method for predicting an investment
significant parameter of a financial instrument, using a computer,
according to the present invention. A model of the bid prices and/or the
ask prices of a financial instrument, based on a set of historical quotes
of the bid and/or ask prices, is provided, step 200. This model may be
desirably generated using an exemplary method of FIG. 1 described above.

[0054]At least one of a bid stream of the bid prices of the financial
instrument or an ask stream of the ask prices of the financial instrument
is selected, step 202. This selection may desirably be based on whether
the bid prices, the ask prices, or both of the financial instrument are
modeled by the model provided in step 200. Although not necessary, for
many financial instruments, particularly high trade volume financial
instruments, it may be desirable for the quote stream(s) selected in step
202 to be a real time bid stream of the bid prices of the financial
instrument and/or a real time ask stream of the ask prices of the
financial instrument. This may allow for the model to predict,
substantially in real time, at least one of future bid prices of the
financial instrument or future ask prices of the financial instrument,
allowing a technical analysis investor using an exemplary embodiment of
the present invention to react substantially faster to changes in the
financial market. Such improved reaction time may greatly increase the
potential for profits by such an investor.

[0055]Desirably, the selected quote stream(s) may include the same
variables as the series of historical data used for generating the model
provided in step 200. For example, if a historical time series of the bid
prices of the financial instrument, including corresponding bid times is
used during generation of the model, then it is desirable for the quote
stream selected in step 202 to be a bid stream of the financial
instrument, including the bid prices and corresponding bid times. As
another example, if a historical series of the ask prices of the
financial instrument, including corresponding ask sizes is used during
generation of the model, then it is desirable for the quote stream
selected in step 202 to be an ask stream of the financial instrument,
including the ask prices and corresponding ask sizes. Additionally, if
other data is included with the series of historical data used for
generating the model provided in step 200, such as: a time series of the
spread between the historical bid prices and the historical ask prices; a
series of historical trade prices; and/or extrinsic data, it is desirable
for those types of data to be included with the quote stream(s) selected
in step 202.

[0056]The selected quote stream(s) is (are) applied to the model, step
204. As discussed above with reference to the series of historical quotes
used in the exemplary method of FIG. 1, it may be desirable to identify
and remove outliers in the selected quote stream(s). If both bid and ask
streams of the financial instrument, including corresponding bid and ask
times, respectively, are selected in step 202, then a spread stream may
be calculated and used to remove outlying quotes from the bid and ask
streams before they are applied to the model. Such an exemplary method of
identifying and removing outlying quotes is described above with
reference to FIG. 1.

[0057]The model is operated on the selected quote steam(s) to predict at
least one investment significant parameter of the financial instrument,
step 206. If the selected quote stream(s) is (are) real time quote
stream(s), then the model may desirably be operated to predict,
substantially in real time, the desired future quotes, trade prices, FMV,
or other stream of investment significant information about the financial
instrument.

[0058]Whether these predictions are made substantially in real time or
not, it may also be desirable for each of the predicted investment
significant parameters to include a predicted time, for example future
quotes of the financial instrument may desirably include a predicted
quote time (i.e. a bid time or an ask time) associated with the predicted
future quote. Desirably, the predicted time of each future investment
significant parameter prediction may be within a predetermined period of
time after prediction. If the predicted time is too close to the time
that it is predicted, an investor using the exemplary method may not be
able to use the information before it becomes stale, or if the predicted
time is too remote, then it may be desirable for the model to hold the
prediction until closer to the predicted time, in case new information
arrives that may affect the prediction.

[0059]It is noted that such time predictions may not be available if the
selected quote stream(s) only include(s) sequential quotes of the
financial instrument without corresponding quote times. In this
situation, the predicted future investment significant parameter(s) of
the financial instrument may represent the next anticipated value of the
trade, bid, and/or ask price of the financial instrument or a short-term
buy/sell instruction, etc. For many technical analysis investors, this
information may be adequate and removing the additional temporal
variables may significantly simplify the model.

[0060]Another feature that may be desirable is a confidence level
associated with each of the predicted investment significant parameter.
Many financial model types include calculation of such confidence level.
Thus, the model may desirably predict one or more future investment
significant parameter of the financial instrument that includes a
corresponding confidence level.

[0061]The model provided in step 200 may also be dynamically updated using
the selected quote stream(s) as additional training data. The quotes of
the quote streams may be used as additional historic quotes to
continually refine the estimate(s) of the variable parameter(s) of the
model using the exemplary method of FIG. 1.

[0062]FIG. 3 illustrates an exemplary method for performing automated
trades of at least one financial instrument, using a computer, according
to the present invention. The financial instrument(s) may be selected
from a single type of financial instrument such as publicly traded
stocks, for example, or may include a number of financial instruments
selected from one or more types of financial instruments, such as stocks,
bonds, commodities, currencies, equities, derivatives, and futures.

[0063]At least one investment significant parameter of each financial
instrument is predicted, step 300. The predicted investment significant
parameter(s) of each financial instrument are predicted using at least
one of a bid stream of the bid prices of the corresponding financial
instrument or an ask stream of the ask prices of the corresponding
financial instrument. Desirably, the predicted investment significant
parameter(s) may be determined using one or more of the exemplary methods
of FIGS. 1 and 2 as described above. The quote stream(s) used may include
real time quote streams of each financial instrument or may include some
quote streams that are not provided in real time. As described above the
predictions may utilize additional data, such as: spread streams for one
or more of the financial instruments; trade price streams for one or more
of the financial instruments; and/or extrinsic data, including market
indices or related financial instruments.

[0064]Additional prediction information such as predicted times and
corresponding confidence levels of each of the one or more corresponding
investment significant parameter may be desirably provided as well for
one or more of the financial instruments.

[0065]If the investment significant parameter(s) predicted in step 300 are
numerical values, then, for each of the financial instruments, the
predicted investment significant parameter(s) are compared to at least
one of the most recent corresponding value(s) of the corresponding
financial instrument to determine trend data of the corresponding
financial instrument, step 302. If the predicted investment significant
parameter(s) for a financial instrument include corresponding parameter
confidence level(s), the trend data determined for the financial
instrument may desirably include a corresponding trend confidence level
based on the corresponding parameter confidence level. The buy criterion
and the sell criterion for a given financial instrument may be varied
based on its trend confidence level. Alternatively (or additionally), the
trend confidence level(s) of the financial instrument(s) may be tracked
to help determine when to place buy and/or sell orders.

[0066]If more than one financial instrument is being traded using the
exemplary method of FIG. 3, then an exemplary joint trade strategy
approach may be used to determine the trend data corresponding to each of
the financial instruments in step 302. In this exemplary approach, for
each of the financial instruments, the predicted investment significant
parameter(s) are compared to at least one most recent corresponding value
of the corresponding financial instrument to determine a predicted change
in the predicted parameter of the corresponding financial instrument. The
plurality of predicted changes in the predicted parameters determined may
be analyzed to formulate a joint trade strategy for the financial
instruments over a predetermined period time to maximize anticipated
return. The trend data corresponding to each of the financial instruments
may then be set based on the joint trade strategy.

[0068]The corresponding trend data of each financial instrument is
compared to the buy criterion of that financial instrument, step 304. If
the corresponding trend data of a financial instrument does not meet the
corresponding buy criterion, then the trend data is compared to the sell
criterion of that financial instrument, step 308. As described above the
buy and/or sell criteria may be varied based on a trend confidence level
of the financial instrument. Alternatively, these criteria may be
predetermined, based on a priori knowledge of the financial instrument or
financial market, or may be determined by the corresponding model of the
financial instrument. Additional factors may also affect these criteria.
For example, the amount of cash, or credit, available to purchase
financial instruments may affect the buy criteria of the financial
instrument and whether any quantity of a given financial instrument is
owned may affect the sell criteria of that financial instrument.

[0069]It is noted that the order of steps 304 and 308 may be reversed, or
these steps may be performed substantially simultaneously without
departing from the present invention.

[0070]If the buy criterion is met in step 304 for a given financial
instrument, then a buy order for that financial instrument is
automatically placed, step 306. Likewise, if the sell criterion is met in
step 308 for a given financial instrument, then a buy order for that
financial instrument is automatically placed, step 310. If neither
criterion is met, then no order is place for the given financial
instrument, step 312.

[0071]When either a buy order is placed in step 306 or a sell order is
placed in step 310, a buy size of the buy order or a sell size of the
sell order may desirably be determined. The buy size may be set to a
predetermined size, a predetermined total price, or may be based on the
amount of cash, credit, and/or other liquid assets available for the
purchase. The sell size may be set to a predetermined size, a
predetermined total price, or may be based on the quantity of the
financial instrument available for sale by the investor. If a
corresponding trend confidence level has been determined, then the buy
size of the buy order, or the sell size of the sell order, may be
determined based on the corresponding trend confidence level.

[0072]Alternatively, a buy size and a sell size for each financial
instrument may be determined based on a comparison of the trend data and
the buy criterion of the financial instrument. A buy order is then
automatically placed for each financial instrument for which the buy size
is greater than zero and a sell order is automatically placed for each
financial instrument for which the sell size is greater than zero.

[0073]If the investment significant parameter(s) predicted in step 300 are
non-numerical values, e.g. buy/sell instructions and/or winner and loser
predictions, then the predicted investment significant parameter(s) may
be used directly to determine what trade orders should be placed. If the
predicted investment significant parameter(s) for a financial instrument
include corresponding parameter confidence level(s), then these parameter
confidence level(s) may be used to determine the size of any trade orders
that are to be placed. In the case where more than one financial
instrument is being traded, the parameter confidence level(s) may be used
to develop a joint trade strategy among the financial instruments that
may include determining the size of any trade orders to be placed.

[0074]It is noted that both numerical and non-numerical investment
significant parameters may be predicted by the exemplary model in step
300 of FIG. 3. In this situation, the investment significant parameters
may be used in combination to determine a (joint) trade strategy. In
determining a trade strategy in this manner, each investment significant
parameter being given a weight in deciding whether to place buy or sell
orders. Alternatively, different investment significant parameters may be
used for different determinations, e.g. a buy/sell instruction may be
used to determine whether to place an order to buy or sell the
corresponding financial instrument and the predicted profit of each trade
may be used to determine order sizes between several financial
instruments.

[0075]The various exemplary embodiment of the present invention may be
carried out through the use of a general-purpose computer system
programmed to perform the steps of the exemplary methods described above
with reference to FIGS. 1, 2, and 3. Exemplary general-purpose computer
systems may include personal computers, work stations, distributed
processing computer networks, and parallel processing computer systems.
Parallel or distributed processing may be desirable for substantially
real time applications involving the substantially concurrent prediction
of future quotes for a plurality of financial instruments. Dedicated
special-purpose computing systems may also be designed for performing
exemplary methods of the present invention as well.

[0076]Additionally, it is contemplated that the methods previously
described may be carried out within a general purpose computer system
instructed to perform these functions by means of a computer-readable
medium. Such computer-readable media include: integrated circuits,
magnetic and optical storage media, as well as audio-frequency, radio
frequency, and optical carrier waves.

[0077]Although the invention is illustrated and described herein with
reference to specific embodiments, the invention is not intended to be
limited to the details shown. Rather, various modifications may be made
in the details within the scope and range of equivalents of the claims
and without departing from the invention.